Non-melanoma skin cancer segmentation for histopathology dataset.

Densely labelled segmentation data for digital pathology images is costly to produce but is invaluable to training effective machine learning models. We make available 290 hand-annotated histopathology tissue sections of the 3 most common skin cancers; basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and intraepidermal carcinoma (IEC). These non-melanoma skin cancers constitute over 90% of all skin cancer diagnoses and hence this dataset gives an opportunity to the scientific community to benchmark analytic methodologies on a significant portion of the dermatopathology workflow. The data represents typical cases of the three cancer types (not requiring a differential diagnosis) across shave, punch and excision biopsy contexts. Each image is accompanied with a segmentation mask which characterizes the section into 12 tissue types, specifically: keratin, epidermis, papillary dermis, reticular dermis, hypodermis, inflammation, glands, hair follicles and background, as well as BCC, SCC and IEC. Included also are cancer margin measurements to work towards automated assessment of surgical margin clearance and tumour invasion. This leaves open many opportunities for researchers to utilize or extend the dataset, building upon recent work on image analysis problems in skin cancer (Thomas et al., 2021).

Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.

We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constitute more than 90% of diagnoses, we demonstrate that the majority of dermatopathology work is amenable to automatic machine analysis. A major feature of this work is characterising the tissue by classifying it into 12 meaningful dermatological classes, including hair follicles, sweat glands as well as identifying the well-defined stratified layers of the skin. These provide highly interpretable outputs as the network is trained to represent the problem domain in the same way a pathologist would. While this enables a high accuracy of whole image classification (93.6-97.9%), by characterising the full context of the tissue we can also work towards performing routine pathologist tasks, for instance, orientating sections and automatically assessing and measuring surgical margins. This work seeks to inform ways in which future computer aided diagnosis systems could be applied usefully in a clinical setting with human interpretable outcomes.

1x11 few-mode fiber wavelength selective switch using photonic lanterns.

We demonstrate an 11 port count wavelength selective switch (WSS) supporting spatial superchannels of three spatial modes, based on the combination of photonic lanterns and a high-port count single-mode WSS.

Spectral modeling of channel band shapes in wavelength selective switches.

A model for characterizing the spectral response of the passband of Wavelength Selective Switches (WSS) is presented. We demonstrate that, in contrast to the commonly used supergaussian model, the presented model offers a more complete match to measured results, as it is based on the physical operation of the optical system. We also demonstrate that this model is better suited for calculation of WSS channel bandwidths, as well as predicting the final bandwidth of cascaded WSS modules. Finally, we show the utility of this model in predicting channel shapes in flexible bandwidth WSS channel plans.

Efficient 550-600-nm tunable laser based on noncritically phase-matched frequency doubling of room-temperature LiF:F2- in lithium triborate.

We report on efficient conversion of a 1064-nm Nd:YAG laser to tunable visible light. The conversion scheme uses noncritically phase-matched second-harmonic generation of a pulsed Nd:YAG-pumped LiF:F2- laser in lithium triborate. Optimization yields 42% LiF:F2- laser efficiency and 56% frequency-doubling efficiency, providing >20% conversion from 1064 nm to broadly tunable visible output. A dramatic sensitivity of laser efficiency to pump pulse duration is recorded, with a fundamental efficiency improvement of over 30-fold measured for an increase in pump pulse duration from 5 to 34 ns.

Analysis of the sequence and expression of a second putative acetylcholinesterase cDNA from organophosphate-susceptible and organophosphate-resistant cattle ticks.

The cattle tick, Boophilus microplus, is a major pest of cattle in Australia, Central and South America, and parts of Africa and Asia. Control of ticks with organophosphates (OPs) and carbamates, which target acetylcholinesterases (AChE), led to evolution of resistance to these pesticides. Alleles at the locus studied here, AChE2, from OP-susceptible female ticks from Australia and Mexico differed at 46 of 1689 nucleotide positions (20 putative amino acid differences) whereas alleles from three strains of OP-resistant ticks from Australia differed with the allele from the Australian susceptible ticks at six to 13 nucleotide positions (three to six putative amino acid differences). However, the role, if any, of these polymorphisms in the OP-resistance phenotype is unknown. Certainly none of the polymorphisms correspond to sites in AChE that are involved in catalysis or binding of acetylcholine in other organisms. Both of the AChE loci of B. microplus, AChE1 and AChE2, are apparently expressed in synganglia; AChE1 is also expressed in salivary glands and ovaries, in OP-susceptible and OP-resistant ticks. This seems to contradict studies of enzyme kinetics, which indicated that only one form of AChE was present in the synganglia, the site of the action of OPs, in this species of tick.